BG3 Antibody
Description
Research Applications and Functional Insights
BG3 has been instrumental in elucidating VACV assembly mechanisms.
Functional Findings:
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Immunoprecipitation: BG3 precipitates p4a and smaller fragments (60–75 kDa), likely representing intermediate processing products .
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Immunofluorescence: Stains viral factories and virion-sized particles, aiding in visualizing viral assembly sites .
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Virion Packaging: The 4a fragment’s tight packaging in virion cores limits BG3’s accessibility, explaining its preference for p4a in assays .
Comparative Analysis with Other Antibodies
While BG3 targets VACV A10, other anti-VACV antibodies (e.g., anti-B5 or anti-L1) focus on distinct viral proteins. Unlike BG3, which recognizes conformational epitopes, many antibodies bind linear epitopes, enabling broader applications in denatured protein assays .
Limitations and Research Implications
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Conformational Specificity: Limits utility in Western blotting unless high antigen concentrations are used .
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Viral Morphogenesis Studies: BG3’s specificity for A10 makes it a critical tool for tracking virion maturation and core protein dynamics .
Future Directions
Further studies could explore BG3’s utility in:
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High-resolution imaging of VACV replication compartments.
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Engineering recombinant antibodies for enhanced affinity to processed 4a fragments.
Product Specs
Constituents: 50% Glycerol, 0.01M PBS, pH 7.4
Target Background
- Research indicates that constitutive plasmodesmata (Pd)-associated beta-1,3-Glucanases (BG), unlike the stress-regulated extracellular pathogenesis-related (PR)-BG, are directly involved in regulating callose levels at Pd and influencing the cell-to-cell transport and spread of viruses. PMID: 23656331
Q&A
Here’s a structured collection of FAQs for researchers working with BG3 antibodies, organized by scientific depth and methodological focus:
Advanced Research Questions
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How to resolve contradictions in BG3 antibody reactivity across studies?
For structural discrepancies, use Rosetta-derived scoring matrices to predict antibody-antigen interactions or S3AI models for interpretable binding predictions .
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What computational methods improve BG3 antibody developability?
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Physics-based docking: Optimize HCDR3 loops for antigen affinity while retaining structural homology
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AI-driven pipelines: Combine sequence landscapes and developability filters (e.g., solubility, aggregation)
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In silico affinity maturation: Engineer mutations (e.g., K36 dimethylation) to rescue binding to escaped viral variants
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Methodological Guidance Table
Data Interpretation
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Why do BG3 antibodies show dual bands (17 kDa and 42 kDa) in Western blot?
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How to benchmark BG3 antibody neutralization potency?
Use plaque reduction neutralization tests (PRNT) with:
